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Project-2-Python-For-Data-Analysis-BadDrivers

End-to-end EDA project on the FiveThirtyEight Bad Drivers dataset | Analyzing fatal collision patterns across U.S. states using Python, Pandas, Matplotlib & Seaborn

πŸš— Exploratory Data Analysis β€” Bad Drivers Dataset

A structured EDA project on the Bad Drivers dataset from FiveThirtyEight, completed as part of the Python for Data Analysis course.

πŸ“Œ Objective

Explore fatal collision patterns across U.S. states and uncover key relationships between speeding, alcohol impairment, insurance premiums, and accident rates.

πŸ“‚ Dataset

  • Source: FiveThirtyEight β€” bad-drivers.csv
  • Records: 51 U.S. states
  • Features: fatal_collisions, pct_speeding, pct_alcohol, pct_not_distracted, pct_no_prev_accidents, insurance_premium, insurance_losses

πŸ› οΈ Tools & Libraries

Library Purpose
Pandas Data loading, cleaning, and exploration
NumPy Numerical operations
Matplotlib Base plotting
Seaborn Statistical visualizations

πŸ“Š Analysis Structure

  1. Imports & Setup
  2. Load Dataset
  3. Rename Columns
  4. First Look (head / tail)
  5. Data Structure & Info
  6. Missing Values Check
  7. Descriptive Statistics
  8. Feature Engineering β€” Speed Level
  9. Univariate Analysis β€” Numerical
  10. Univariate Analysis β€” Categorical
  11. Bivariate Analysis β€” Numerical vs Numerical
  12. Bivariate Analysis β€” Numerical vs Categorical
  13. Multivariate Analysis
  14. Correlation Heatmap
  15. Pairplot
  16. Key Insights

πŸ’‘ Key Insights

  • Speeding alone is not the strongest predictor of fatal collisions β€” alcohol impairment shows a clearer impact
  • North Dakota and South Carolina are clear outliers with the highest fatal collision rates
  • Insurance premiums don't always reflect actual collision rates β€” pricing involves more complex factors
  • Fatal collision distribution is right-skewed β€” most states fall between 10–20 collisions per billion miles
  • Medium-speed states show the highest variance in collision rates

πŸš€ How to Run

git clone https://github.com/ammarelsayed-2a/Project-2-Python-For-Data-Analysis-BadDrivers.git
cd Project 2 Python-For-Data-Analysis-BadDrivers
jupyter notebook "Project 2 BadDrivers.ipynb"

πŸ‘€ Author

Ammar Elsayed β€” Python for Data Analysis | 2026
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End-to-end EDA project on the FiveThirtyEight Bad Drivers dataset | Analyzing fatal collision patterns across U.S. states using Python, Pandas, Matplotlib & Seaborn

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